Intelligent Method for Identifying Damage of Steel Members with Localized Random Pitting Based on Convolutional Neural Network

doi:10.12141/j.issn.1000-565X.230685

Abstract

Abstract:

Pitting induced by the marine environment has a significant impact on the safety of steel structures and its form exhibits a strong multi-scale and multi-parameter randomness. In order to effectively detect and identify damage in actual engineering, this paper systematically investigates local random pitting of steel members via experimental study, numerical simulation, and theoretical analysis based on convolutional neural networks. Firstly, under the premise of following the distribution model of pitting corrosion pit depth and the time-varying model of pitting corrosion pit diameter, the boundary and cross restrictions were imposed on the position distribution of corrosion pits using multi-parameter local random pitting numerical model. Python was utilized to generate randomness in the size, location, and number of pits, allowing Abaqus to generate a large number of finite element models of steel plates with varying rust locations and rust rates, and the mode shape samples of each finite element model were obtained. Then, the finite element model was used as a test prototype, and a large number of samples of the first six-order vibration patterns obtained from numerical tests were used to train a convolutional neural network model for identifying damage location. The accuracy of the model was verified using the finite element data set. Finally, the vibration results of the ruler test were used to further verify the accuracy of the convolutional neural network model. The study shows that the model fully considers the randomness of pitting corrosion in aspects such as shape parameters and position coordinates. The parameters are reasonable, close to the actual pitting corrosion situation in reality, and the recognition accuracy is relatively high. In numerical tests, the model achieved 95.9% accuracy in identifying pitting damage to the real area and its adjacent areas, and 81.2% accuracy in full-scale tests, meeting the requirements for the practical intelligent application of identifying steel component damage.

Key words: steel structure, local pitting damage, damage identification, convolutional neural network

CLC Number:

TU392

QIANG Xuhong, TIAN Weixiao, JIANG Xu, et al. Intelligent Method for Identifying Damage of Steel Members with Localized Random Pitting Based on Convolutional Neural Network[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(11): 43-54.

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